Responses to commentaries on "The gene: An appraisal"

The central conclusions of "The Gene: An Appraisal" are that genetic variance does not underpin biological evolution, and, therefore, that genes are not Mendel's units of inheritance. In this response, I will address the criticisms I have received via commentaries on that paper by defending the following statements: 1. Epistasis does not explain the power-law fitness profile of the Long-Term Evolution Experiment (LTEE). The data from the evolution of natural systems displays the power-law form ubiquitously. Epistasis plays no role in evolution. 2. The common characteristics of living things (natural systems) are described by the principle of least action in de Maupertuis's original form, which is synonymous with the 2nd law of thermodynamics and Newton's 2nd law of motion in its complete form, i.e., F = dp/dt. Organisms strive to achieve free energy balance with their environments. 3. Based on an appraisal of the scientific environment between 1880 and 1911, I conclude that Johannsen's genotype conception was perhaps, the only option available to him. 4. The power-law fitness profile of the LTEE falsifies Fisher's Genetical Theory of Natural Selection, Johannsen's genotype conception, and the idea that 'Darwinian evolution' is an exception to the generic thermodynamic process of evolution in natural systems. 5. The use of the technique of genome-wide association to identify the causes and the likelihoods of inherited common diseases and behavioural traits is a 'wild goose chase' because genes are not the units of inheritance.

Pseudomonas aeruginosa Y12 play positive roles regulating larval gut communities when housefly encountered copper stress

Heavy metal contamination has become a global concern that threatens the lives of animals and insects throughout the food chain. Pseudomonas is a commonly found genus of bacteria that colonizes the intestines of insects and constitutes a necessary part of the insect gut microbiota. This research analyzed the influence of different concentrations of Cu²⁺ on housefly larval development, gut microbial structure and antioxidant defense system, and investigated the regulatory mechanism of P. aeruginosa Y12 on the gut microbiota when houseflies were exposed to Cu²⁺. We found that adding Cu²⁺ to the larval diet inhibited larval growth, while the mixed addition of P. aeruginosa Y12 and Cu²⁺ to the diet reduced the inhibitory effects of Cu²⁺ on larval growth. Oral administration of Cu²⁺ significantly changed the gut community structure and increased larval gut bacterial diversity. In vitro analysis revealed that P. aeruginosa Y12 showed Cu²⁺ adsorption effects and increased Cu²⁺ aggregation. The mixed addition of low concentrations of P. aeruginosa Y12 and Cu²⁺ to the larval diet caused a dynamic shift in the gut microbiota and resulted in a novel gut community structure with an increase in beneficial bacteria and a decrease in pathogenic bacteria. Furthermore, P. aeruginosa Y12 treatment influenced the activity of antioxidant enzymes in housefly larvae, indicating that the addition of P. aeruginosa Y12 to the larval diet could further influence the antioxidant system through P. aeruginosa Y12-Cu²⁺ interactions. In conclusion, our research revealed that intestinal flora dysbiosis was the essential reason why copper inhibits housefly larval growth. However, proper supplementation with P. aeruginosa Y12 played positive roles in regulating larval gut communities and protecting insects from toxic heavy metals.

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs unsupervised) per application, as well as on their system boundaries (engineered vs natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.

The relative impact of toxic heavy metals (THMs) (arsenic (As), cadmium (Cd), chromium (Cr)(VI), mercury (Hg), and lead (Pb)) on the total environment: an overview

Certain five heavy metals viz. arsenic (As), cadmium (Cd), chromium (Cr)(VI), mercury (Hg), and lead (Pb) are non-threshold toxins and can exert toxic effects at very low concentrations. These heavy metals are known as most problematic heavy metals and as toxic heavy metals (THMs). Several industrial activities and some natural processes are responsible for their high contamination in the environment. In recent years, high concentrations of heavy metals in different natural systems including atmosphere, pedosphere, hydrosphere, and biosphere have become a global issue. These THMs have severe deteriorating effects on various microorganisms, plants, and animals. Human exposure to the THMs may evoke serious health injuries and impairments in the body, and even certain extremities can cause death. In all these perspectives, this review provides a comprehensive account of the relative impact of the THMs As, Cd, Cr(VI), Hg, and Pb on our total environment.